R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

1 R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

1.1 R-project

https://cran.r-project.org/

1.2 RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

1.2.1 RStudio eklentileri

Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

# devtools::install_github("rstudio/addinexamples", type = "source")

1.3 X11

https://www.xquartz.org/

1.4 Java OS

https://support.apple.com/kb/dl1572

2 R zor şeyler için kolay, kolay şeyler için zor

R makes easy things hard, and hard things easy
Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf

#RStats — There are always several ways to do the same thing… nice example on with the identity matrix by @TeaStats https://t.co/O3GXdPiM32
— Colin Fay 🤘 (@_ColinFay) April 1, 2019

3 R paketleri

3.1 Neden paketler var

I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

3.2 Paketleri nereden bulabiliriz

Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.html
CRAN Task Views
https://cran.r-project.org/web/views/
Bioconductor
https://www.bioconductor.org
RecommendR
http://recommendr.info/
pkgsearch
CRAN package search
https://github.com/metacran/pkgsearch
CRANsearcher
https://github.com/RhoInc/CRANsearcher
Awesome R
https://awesome-r.com/

3.3 Kendi paket evrenini oluştur

pkgverse: Build a Meta-Package Universe
https://cran.r-project.org/web/packages/pkgverse/index.html

3.4 R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")

# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)

4 R için yardım bulma

# ?mean
# ??efetch
# help(merge)
# example(merge)

Vignette

RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow

https://stackoverflow.com/

Google uygun anahtar kelime

How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
— Emily Bovee (@ebovee09) August 10, 2018

Google’da ararken [R] yazmak da işe yarayabiliyor.

searcher package 📦

Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/

Twitter

https://twitter.com/hashtag/rstats?src=hash

Reproducible Examples

Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX
— ZhiYang (@zhiiiyang) October 18, 2018

Keeping up to date with R news
https://masalmon.eu/2019/01/25/uptodate/

5 R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

6 RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

6.1 Excel

6.2 SPSS

6.3 csv

7 Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018

# library(nycflights13)
# summary(flights)

View(data)

data

head

tail

glimpse

str

skimr::skim()

8 Veriyi değiştirme

8.1 Veriyi kod ile değiştirelim

8.2 Veriyi eklentilerle değiştirme

8.3 RStudio aracılığıyla recode

questionr paketi kullanılacak

https://juba.github.io/questionr/articles/recoding_addins.html

9 Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

Parsed with column specification:
cols(
  Sepal.Length = [32mcol_double()[39m,
  Sepal.Width = [32mcol_double()[39m,
  Petal.Length = [32mcol_double()[39m,
  Petal.Width = [32mcol_double()[39m,
  Species = [31mcol_character()[39m
)

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)


 DESCRIPTIVES

 Descriptives                                          
 ───────────────────────────────────────────────────── 
                          Species       Sepal.Length   
 ───────────────────────────────────────────────────── 
   N                      setosa                  50   
                          versicolor              50   
                          virginica               50   
   Missing                setosa                   0   
                          versicolor               0   
                          virginica                0   
   Mean                   setosa                5.01   
                          versicolor            5.94   
                          virginica             6.59   
   Std. error mean        setosa              0.0498   
                          versicolor          0.0730   
                          virginica           0.0899   
   Median                 setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   Mode                   setosa                5.00   
                          versicolor            5.50   
                          virginica             6.30   
   Sum                    setosa                 250   
                          versicolor             297   
                          virginica              329   
   Standard deviation     setosa               0.352   
                          versicolor           0.516   
                          virginica            0.636   
   Variance               setosa               0.124   
                          versicolor           0.266   
                          virginica            0.404   
   Range                  setosa                1.50   
                          versicolor            2.10   
                          virginica             3.00   
   Minimum                setosa                4.30   
                          versicolor            4.90   
                          virginica             4.90   
   Maximum                setosa                5.80   
                          versicolor            7.00   
                          virginica             7.90   
   Skewness               setosa               0.120   
                          versicolor           0.105   
                          virginica            0.118   
   Std. error skewness    setosa               0.337   
                          versicolor           0.337   
                          virginica            0.337   
   Kurtosis               setosa              -0.253   
                          versicolor          -0.533   
                          virginica           0.0329   
   Std. error kurtosis    setosa               0.662   
                          versicolor           0.662   
                          virginica            0.662   
   25th percentile        setosa                4.80   
                          versicolor            5.60   
                          virginica             6.23   
   50th percentile        setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   75th percentile        setosa                5.20   
                          versicolor            6.30   
                          virginica             6.90   
 ─────────────────────────────────────────────────────

# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

9.1 summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")

9.1.1 Frequencies

9.1.1.1 iris$Species

Type: Factor

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)

‘omit.headings’ argument has been replaced by ‘headings’; setting headings = FALSE

	Freq	%	% Cum.
setosa	50	33.33	33.33
versicolor	50	33.33	66.67
virginica	50	33.33	100.00
Total	150	100.00	100.00

with(tobacco, print(ctable(smoker, diseased), method = 'render'))

Cross-Tabulation, Row Proportions

smoker * diseased

Data Frame: tobacco

	diseased
smoker	Yes				No				Total
Yes	125	(	41.9%	)	173	(	58.1%	)	298	(	100.0%	)
No	99	(	14.1%	)	603	(	85.9%	)	702	(	100.0%	)
Total	224	(	22.4%	)	776	(	77.6%	)	1000	(	100.0%	)

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))

‘omit.headings’ will disappear in future releases; use ‘headings’ instead

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

summarytools::descr(iris, style = "rmarkdown")

Non-numerical variable(s) ignored: Species ### Descriptive Statistics
#### iris
N: 150

	Petal.Length	Petal.Width	Sepal.Length	Sepal.Width
Mean	3.76	1.20	5.84	3.06
Std.Dev	1.77	0.76	0.83	0.44
Min	1.00	0.10	4.30	2.00
Q1	1.60	0.30	5.10	2.80
Median	4.35	1.30	5.80	3.00
Q3	5.10	1.80	6.40	3.30
Max	6.90	2.50	7.90	4.40
MAD	1.85	1.04	1.04	0.44
IQR	3.50	1.50	1.30	0.50
CV	0.47	0.64	0.14	0.14
Skewness	-0.27	-0.10	0.31	0.31
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-1.42	-1.36	-0.61	0.14
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")

‘omit.headings’ argument has been replaced by ‘headings’; setting headings = FALSE Non-numerical variable(s) ignored: Species

	Mean	Std.Dev	Min	Median	Max
Petal.Length	3.76	1.77	1.00	4.35	6.90
Petal.Width	1.20	0.76	0.10	1.30	2.50
Sepal.Length	5.84	0.83	4.30	5.80	7.90
Sepal.Width	3.06	0.44	2.00	3.00	4.40

# view(dfSummary(iris))

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")

text graphs are displayed; set ‘tmp.img.dir’ parameter to activate png graphs NAs introduced by coercion### Data Frame Summary
#### tobacco
Dimensions: 1000 x 9
Duplicates: 2

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
1	gender [factor]	1. F 2. M	489 (50.0%) 489 (50.0%)	IIIIIIIIII IIIIIIIIII	978 (97.8%)	22 (2.2%)
2	age [numeric]	Mean (sd) : 49.6 (18.3) min < med < max: 18 < 50 < 80 IQR (CV) : 32 (0.4)	63 distinct values	. . . . . : : : : : : . : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :	975 (97.5%)	25 (2.5%)
3	age.gr [factor]	1. 18-34 2. 35-50 3. 51-70 4. 71 +	258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%)	IIIII IIII IIIIII III	975 (97.5%)	25 (2.5%)
4	BMI [numeric]	Mean (sd) : 25.7 (4.5) min < med < max: 8.8 < 25.6 < 39.4 IQR (CV) : 5.7 (0.2)	974 distinct values	: : : : : : : : : : : : . : : : : : .	974 (97.4%)	26 (2.6%)
5	smoker [factor]	1. Yes 2. No	298 (29.8%) 702 (70.2%)	IIIII IIIIIIIIIIIIII	1000 (100%)	0 (0%)
6	cigs.per.day [numeric]	Mean (sd) : 6.8 (11.9) min < med < max: 0 < 0 < 40 IQR (CV) : 11 (1.8)	37 distinct values	: : : : : . . . . . .	965 (96.5%)	35 (3.5%)
7	diseased [factor]	1. Yes 2. No	224 (22.4%) 776 (77.6%)	IIII IIIIIIIIIIIIIII	1000 (100%)	0 (0%)
8	disease [character]	1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ]	36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.5%)	III III I I I I I I I I	222 (22.2%)	778 (77.8%)
9	samp.wgts [numeric]	Mean (sd) : 1 (0.1) min < med < max: 0.9 < 1 < 1.1 IQR (CV) : 0.2 (0.1)	0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded	IIIII IIII IIIIII III	1000 (100%)	0 (0%)


# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")

Non-numerical variable(s) ignored: Species ### Descriptive Statistics
#### iris
Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	1.46	0.17	1.00	1.50	1.90
Petal.Width	0.25	0.11	0.10	0.20	0.60
Sepal.Length	5.01	0.35	4.30	5.00	5.80
Sepal.Width	3.43	0.38	2.30	3.40	4.40

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80

# view(iris_stats_by_species)

data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

9.1.2 Descriptive Statistics

9.1.2.1 BMI by age.gr

Data Frame: tobacco
N: 258

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

‘omit.headings’ will disappear in future releases; use ‘headings’ instead

	Mean	Std.Dev	Min	Median	Max
18-34	23.84	4.23	8.83	24.04	34.84
35-50	25.11	4.34	10.35	25.11	39.44
51-70	26.91	4.26	9.01	26.77	39.21
71 +	27.45	4.37	16.36	27.52	38.37

tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')

what.is(iris)

function ‘is’ appears not to be S3 generic; found functions that look like S3 methods‘>=’ not meaningful for factors$properties

$attributes.lengths names class row.names 5 1 150

$extensive.is [1] “is.data.frame” “is.list” “is.object”
[4] “is.recursive” “is.unsorted”

freq(tobacco$gender, style = 'rmarkdown')

### Frequencies  
#### tobacco$gender  
**Type:** Factor  

|     &nbsp; | Freq | % Valid | % Valid Cum. | % Total | % Total Cum. |
|-----------:|-----:|--------:|-------------:|--------:|-------------:|
|      **F** |  489 |   50.00 |        50.00 |   48.90 |        48.90 |
|      **M** |  489 |   50.00 |       100.00 |   48.90 |        97.80 |
| **\<NA\>** |   22 |         |              |    2.20 |       100.00 |
|  **Total** | 1000 |  100.00 |       100.00 |  100.00 |       100.00 |

print(freq(tobacco$gender), method = 'render')

Frequencies

tobacco$gender

Type: Factor

		Valid		Total
gender	Freq	%	% Cum.	%	% Cum.
F	489	50.00	50.00	48.90	48.90
M	489	50.00	100.00	48.90	97.80
<NA>	22			2.20	100.00
Total	1000	100.00	100.00	100.00	100.00

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

9.2 skimr

library(skimr)
skim(df)

9.3 DataExplorer

library(DataExplorer)
DataExplorer::create_report(df)

9.4 Grafikler

# library(ggplot2)
# library(mosaic)
# mPlot(irisdata)

ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')

9.4.1 Cross-Tabulation, Row Proportions

9.4.1.1 gender * smoker

Data Frame: tobacco

	smoker	Yes	No	Total
gender
F		147 (30.1%)	342 (69.9%)	489 (100.0%)
M		143 (29.2%)	346 (70.8%)	489 (100.0%)
<NA>		8 (36.4%)	14 (63.6%)	22 (100.0%)
Total		298 (29.8%)	702 (70.2%)	1000 (100.0%)

print(ctable(tobacco$gender, tobacco$smoker), method = 'render')

Cross-Tabulation, Row Proportions

gender * smoker

Data Frame: tobacco

	smoker
gender	Yes				No				Total
F	147	(	30.1%	)	342	(	69.9%	)	489	(	100.0%	)
M	143	(	29.2%	)	346	(	70.8%	)	489	(	100.0%	)
<NA>	8	(	36.4%	)	14	(	63.6%	)	22	(	100.0%	)
Total	298	(	29.8%	)	702	(	70.2%	)	1000	(	100.0%	)

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018

Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018

9.5 Tablolar

10 Bazı arayüzler

Link

10.1 Rcmdr

library(Rcmdr)

Rcmdr::Commander()

A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

10.2 jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

11 R nereden öğrenilir

12 Sonraki Konular

RStudio ile GitHub
Hipotez testleri
R Markdown ve R Notebook ile tekrarlanabilir rapor

13 Geri Bildirim

Geri bildirim için tıklayınız: Geri bildirim formu

# Save Final Data

saved data after analysis to `Data-After-Analysis.xlsx`.

saveRDS(mydata, "Data-After-Analysis.rds")

writexl::write_xlsx(mydata, "Data-After-Analysis.xlsx")

file.info("Data-After-Analysis.xlsx")$ctime

14 Libraries Used

citation()

citation("tidyverse")
citation("foreign")
citation("tidylog")
citation("janitor")
citation("jmv")
citation("tangram")
citation("finalfit")
citation("summarytools")
citation("ggstatplot")
citation("readxl")

report::cite_packages(session = sessionInfo())

sessionInfo()

15 Notes

Completed on 2019-04-20 10:50:57.

Serdar Balci, MD, Pathologist
d rserdarbalci@gmail.com
https://rpubs.com/sbalci/CV

https://sbalci.github.io/
https://github.com/sbalci
—

Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩

---
title: R ile analize başlarken^[Bu bir derlemedir, mümkün mertebe alıntılara referans
  vermeye çalıştım.]
author: "Derleyen [Serdar Balcı, MD, Pathologist](https://sbalci.github.io/)"
date: "`r format(Sys.Date())`"
output:
  html_notebook:
    fig_caption: yes
    highlight: kate
    number_sections: yes
    theme: flatly
    toc: yes
    toc_depth: 5
    toc_float: yes
  pdf_document:
    toc: yes
    toc_depth: '5'
  html_document: 
    fig_caption: yes
    keep_md: yes
    toc: yes
    toc_depth: 5
    toc_float: yes
---

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</script> 




[![](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530113077/Image_2_vfy48b.png)](https://www.datacamp.com/community/tutorials/data-science-pitfalls)


- R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x


# R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

[![What is R?](http://img.youtube.com/vi/XcBLEVknqvY/0.jpg)](http://www.youtube.com/watch?v=XcBLEVknqvY)


## R-project

https://cran.r-project.org/

---

[![](https://ismayc.github.io/talks/ness-infer/img/engine.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#6)

---

## RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

---

[![](http://www-users.york.ac.uk/~er13/RStudio%20Anatomy.svg)](https://buzzrbeeline.blog/2018/07/04/rstudio-anatomy/)



---

### RStudio eklentileri

- Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/


```{r}
# devtools::install_github("rstudio/addinexamples", type = "source")
```


---

## X11

https://www.xquartz.org/

---

## Java OS

https://support.apple.com/kb/dl1572

---


# R zor şeyler için kolay, kolay şeyler için zor


- [R makes easy things hard, and hard things easy](http://r4stats.com/articles/why-r-is-hard-to-learn/)


- Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf




<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr"><a href="https://twitter.com/hashtag/RStats?src=hash&amp;ref_src=twsrc%5Etfw">#RStats</a> — There are always several ways to do the same thing... nice example on with the identity matrix by <a href="https://twitter.com/TeaStats?ref_src=twsrc%5Etfw">@TeaStats</a> <a href="https://t.co/O3GXdPiM32">https://t.co/O3GXdPiM32</a></p>&mdash; Colin Fay 🤘 (@_ColinFay) <a href="https://twitter.com/_ColinFay/status/1112746633467518977?ref_src=twsrc%5Etfw">April 1, 2019</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>










---


# R paketleri


## Neden paketler var

[![](https://ismayc.github.io/talks/ness-infer/img/appstore.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#7)

---

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I love the <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> community.<br>Someone is like, &quot;oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It&#39;s called pbplyr.&quot;<br>What a tribe.</p>&mdash; Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) <a href="https://twitter.com/Frankly_Data/status/1014189095294291968?ref_src=twsrc%5Etfw">July 3, 2018</a></blockquote>

---



https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

---

![](https://blog.mitchelloharawild.com/blog/2018-07-11-user-2018-feature-wall_files/final.jpg)

---

## Paketleri nereden bulabiliriz

- Available CRAN Packages By Name  
https://cran.r-project.org/web/packages/available_packages_by_name.html

- CRAN Task Views  
https://cran.r-project.org/web/views/

- Bioconductor  
https://www.bioconductor.org

- RecommendR  
http://recommendr.info/

- pkgsearch  
CRAN package search  
https://github.com/metacran/pkgsearch

- CRANsearcher  
https://github.com/RhoInc/CRANsearcher  

- Awesome R  
https://awesome-r.com/  


## Kendi paket evrenini oluştur

- pkgverse: Build a Meta-Package Universe  
https://cran.r-project.org/web/packages/pkgverse/index.html


---

## R paket yükleme

```
install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")
```

```{r paket yükleme}
# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")
```


```{r paket cagirma, error=FALSE, message = FALSE, warning = FALSE, eval = TRUE, include = TRUE}
# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)
```



---

# R için yardım bulma


```{r yardım}
# ?mean
# ??efetch
# help(merge)
# example(merge)
```



- Vignette

![](figures/vignette.png)

---

- RDocumentation
https://www.rdocumentation.org

- R Package Documentation
https://rdrr.io/

- GitHub

- Stackoverflow

https://stackoverflow.com/

- Google uygun anahtar kelime



<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">How I use <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <br>h/t <a href="https://twitter.com/ThePracticalDev?ref_src=twsrc%5Etfw">@ThePracticalDev</a> <a href="https://t.co/erRnTG0Ujr">pic.twitter.com/erRnTG0Ujr</a></p>&mdash; Emily Bovee (@ebovee09) <a href="https://twitter.com/ebovee09/status/1028037594947485696?ref_src=twsrc%5Etfw">August 10, 2018</a></blockquote>


---


![](figures/Google-package-name.png)

---



![](figures/Google-start-with-R.png)

---

- Google'da ararken `[R]` yazmak da işe yarayabiliyor.


---

- searcher package 📦


[![](https://camo.githubusercontent.com/12f0e2d18047f1b5f36fbeb09a1d0e548236883f/68747470733a2f2f692e696d6775722e636f6d2f5a7132726736472e676966)](https://github.com/coatless/searcher)



---

- Awesome Cheatsheet
https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/


- Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/




- Twitter

https://twitter.com/hashtag/rstats?src=hash


- Reproducible Examples  

<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Got a question to ask on <a href="https://twitter.com/SlackHQ?ref_src=twsrc%5Etfw">@SlackHQ</a> or post on <a href="https://twitter.com/github?ref_src=twsrc%5Etfw">@github</a>? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by <a href="https://twitter.com/JennyBryan?ref_src=twsrc%5Etfw">@JennyBryan</a>) <a href="https://twitter.com/hashtag/rstat?src=hash&amp;ref_src=twsrc%5Etfw">#rstat</a> <a href="https://t.co/gpuGXpFIsX">pic.twitter.com/gpuGXpFIsX</a></p>&mdash; ZhiYang (@zhiiiyang) <a href="https://twitter.com/zhiiiyang/status/1053006003711569920?ref_src=twsrc%5Etfw">October 18, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


- Keeping up to date with R news  
https://masalmon.eu/2019/01/25/uptodate/  

---

# R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

![](http://www.rstudio.com/images/docs/projects_new.png)

---

# RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

![](https://support.rstudio.com/hc/en-us/article_attachments/206277618/data-import-overview.gif)

---

## Excel

## SPSS

## csv


---

# Veriyi görüntüleme

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Spreadsheet users using <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>:  where&#39;s the data?<a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> users using spreadsheets:  where&#39;s the code?</p>&mdash; Leonard Kiefer (@lenkiefer) <a href="https://twitter.com/lenkiefer/status/1015587475580956672?ref_src=twsrc%5Etfw">July 7, 2018</a></blockquote>



```{r, results="markup"}
# library(nycflights13)
# summary(flights)
```



```
View(data)
```


```
data
```


```
head
```


```
tail
```


```
glimpse
```


```
str
```


```
skimr::skim()
```

---


# Veriyi değiştirme

## Veriyi kod ile değiştirelim

## Veriyi eklentilerle değiştirme

![](figures/change_data.png)


---


## RStudio aracılığıyla recode

*questionr* paketi kullanılacak

![](figures/level_recode.png)


---



https://juba.github.io/questionr/articles/recoding_addins.html


![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_1.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_2.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_3.png)


---

# Basit tanımlayıcı istatistikler

```
summary()
```

```
mean
```

```
median
```

```
min
```

```
max
```

```
sd
```

```
table()
```


```{r descriptive, echo=TRUE, include = TRUE}
library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)
```

---

```{r scatter, echo=TRUE, include=TRUE}
# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

```

## summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, print(ctable(smoker, diseased), method = 'render'))
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::descr(iris, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# view(dfSummary(iris))

```


![](figures/dfsummary.png)



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
dfSummary(tobacco, plain.ascii = FALSE, style = "grid")
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}

# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# view(iris_stats_by_species)
```

![](figures/DescriptiveStatistics.png)

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
what.is(iris)
```

---

```{r}
freq(tobacco$gender, style = 'rmarkdown')
```

---

```{r}
print(freq(tobacco$gender), method = 'render')
```

---

## skimr

```
library(skimr)
skim(df)
```

---

## DataExplorer

```
library(DataExplorer)
DataExplorer::create_report(df)
```


[![](https://static1.squarespace.com/static/58eef8846a4963e429687a4d/t/5bdfc2fb4d7a9c04ee50b7aa/1541391160702/dataExplorerGifLg.gif?format=1500w)](https://www.littlemissdata.com/blog/simple-eda)



---

## Grafikler

```{r}
# library(ggplot2)
# library(mosaic)
# mPlot(irisdata)
```

---


```{r, results="asis"}
ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')
```

---


```{r}
print(ctable(tobacco$gender, tobacco$smoker), method = 'render')
```

```
descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')
```


---



<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Here, building up a <a href="https://twitter.com/hashtag/ggplot2?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot2</a> as slowly as possible, <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>.  Incremental adjustments.  <a href="https://twitter.com/hashtag/rstatsteachingideas?src=hash&amp;ref_src=twsrc%5Etfw">#rstatsteachingideas</a> <a href="https://t.co/nUulQl8bPh">pic.twitter.com/nUulQl8bPh</a></p>&mdash; Gina Reynolds (@EvaMaeRey) <a href="https://twitter.com/EvaMaeRey/status/1029104656763572226?ref_src=twsrc%5Etfw">August 13, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


---


[![](https://raw.githubusercontent.com/dreamRs/esquisse/master/man/figures/esquisse.gif)](https://github.com/dreamRs/esquisse)


<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Dreaming of a fancy <a href="https://twitter.com/hashtag/Rstats?src=hash&amp;ref_src=twsrc%5Etfw">#Rstats</a> <a href="https://twitter.com/hashtag/ggplot?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot</a> <a href="https://twitter.com/hashtag/dataviz?src=hash&amp;ref_src=twsrc%5Etfw">#dataviz</a> but still scared of typing <a href="https://twitter.com/hashtag/code?src=hash&amp;ref_src=twsrc%5Etfw">#code</a>? <a href="https://twitter.com/_pvictorr?ref_src=twsrc%5Etfw">@_pvictorr</a> esquisse package has you covered <a href="https://t.co/1vIDXcVAAF">https://t.co/1vIDXcVAAF</a> <a href="https://t.co/RlTkptnrNv">pic.twitter.com/RlTkptnrNv</a></p>&mdash; Radoslaw Panczak (@RPanczak) <a href="https://twitter.com/RPanczak/status/1047019588658040832?ref_src=twsrc%5Etfw">October 2, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


---

## Tablolar







---

# Bazı arayüzler

Link

## Rcmdr

```
library(Rcmdr)

Rcmdr::Commander()

```


- A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/


---

## jamovi

https://www.jamovi.org/

![![](https://www.jamovi.org/assets/main-screenshot.png)](https://www.jamovi.org/)


https://blog.jamovi.org/2018/07/30/rj.html

![![](https://blog.jamovi.org/assets/images/rj.png)](https://blog.jamovi.org/2018/07/30/rj.html)

---

# R nereden öğrenilir


---

# Sonraki Konular

- RStudio ile GitHub
- Hipotez testleri
- R Markdown ve R Notebook ile tekrarlanabilir rapor


-----

# Geri Bildirim

- Geri bildirim için tıklayınız: _[Geri bildirim formu](https://goo.gl/forms/YjGZ5DHgtPlR1RnB3)_


---

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<div id="disqus_thread"></div>
<script>

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<noscript>Please enable JavaScript to view the <a href="https://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>

---


\pagebreak

---

```
# Save Final Data

saved data after analysis to `Data-After-Analysis.xlsx`.

saveRDS(mydata, "Data-After-Analysis.rds")

writexl::write_xlsx(mydata, "Data-After-Analysis.xlsx")

file.info("Data-After-Analysis.xlsx")$ctime

```

---

\pagebreak


# Libraries Used

```{r}
citation()
```

```
citation("tidyverse")
citation("foreign")
citation("tidylog")
citation("janitor")
citation("jmv")
citation("tangram")
citation("finalfit")
citation("summarytools")
citation("ggstatplot")
citation("readxl")
```


```{r eval=FALSE, include=FALSE}
citation("tidyverse")
citation("foreign")
citation("tidylog")
citation("janitor")
citation("jmv")
citation("tangram")
citation("finalfit")
citation("summarytools")
citation("ggstatplot")
citation("readxl")
```


---


```{r, results='asis'}
report::cite_packages(session = sessionInfo())
```


---



```{r}
sessionInfo()
```

---

\pagebreak



# Notes  

Completed on `r Sys.time()`.  

Serdar Balci, MD, Pathologist  
d  rserdarbalci@gmail.com  
https://rpubs.com/sbalci/CV   

  
https://sbalci.github.io/  
https://github.com/sbalci  
---






R ile analize başlarken1

Derleyen Serdar Balcı, MD, Pathologist

2019-04-20

1 R yükleme

1.1 R-project

1.2 RStudio

1.2.1 RStudio eklentileri

1.3 X11

1.4 Java OS

2 R zor şeyler için kolay, kolay şeyler için zor

3 R paketleri

3.1 Neden paketler var

3.2 Paketleri nereden bulabiliriz

3.3 Kendi paket evrenini oluştur

3.4 R paket yükleme

4 R için yardım bulma

5 R studio ile proje oluşturma

6 RStudio ile veri yükleme

6.1 Excel

6.2 SPSS

6.3 csv

7 Veriyi görüntüleme

8 Veriyi değiştirme

8.1 Veriyi kod ile değiştirelim

8.2 Veriyi eklentilerle değiştirme

8.3 RStudio aracılığıyla recode

9 Basit tanımlayıcı istatistikler

9.1 summarytools

9.1.1 Frequencies

9.1.1.1 iris$Species

Cross-Tabulation, Row Proportions

smoker * diseased

9.1.2 Descriptive Statistics

9.1.2.1 BMI by age.gr

Frequencies

tobacco$gender

9.2 skimr

9.3 DataExplorer

9.4 Grafikler

9.4.1 Cross-Tabulation, Row Proportions

9.4.1.1 gender * smoker

Cross-Tabulation, Row Proportions

gender * smoker

9.5 Tablolar

10 Bazı arayüzler

10.1 Rcmdr

10.2 jamovi

11 R nereden öğrenilir

12 Sonraki Konular

13 Geri Bildirim

14 Libraries Used

15 Notes

R ile analize başlarken¹